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#SMX #21C1 @minderwinter
Charles Midwinter, Collegis Education
Visualizing Attribution
in Living Color
#SMX #21C1 @minderwinter
 When multiple channels or tactics assist
with a conversion, an attribution model is
the set of ...
#SMX #21C1 @minderwinter
 Last Interaction
 Last Non-direct Click
 Last AdWords Click
 First Interaction
 Linear
 Ti...
#SMX #21C1 @minderwinter
Almost anything is better than “Last Click,”
but black boxes aren’t much better.
 No visibility ...
#SMX #21C1 @minderwinter
If you want to understand multi-channel
attribution, the “multi-channel attribution
funnel” repor...
#SMX #21C1 @minderwinter
The object that can summarize these
conversion paths is called an “edge matrix.”
 Usually used f...
#SMX #21C1 @minderwinter
Consider the following conversion paths:
 A > C > B > C
 A > B
 B > C
Edge Matrix Example 1/3
#SMX #21C1 @minderwinter
In words
 A
 referred to C once
 referred to B once
 B
 referred to C twice
 C
 referred t...
#SMX #21C1 @minderwinter
As an “Edge Matrix”
Edge Matrix Example 3/3
A B C
A 0 1 1
B 0 0 2
C 0 1 0
#SMX #21C1 @minderwinter
Just use my handy dandy Python script.
 Go to:
 traffictheory.org/smx-2015
 Download the scrip...
#SMX #21C1 @minderwinter
To visualize the “Edge Matrix” as a Node
Graph, you’ll need Gephi, open source
graph software.
 ...
#SMX #21C1 @minderwinter
 How do we turn this spaghetti into
something useful?
The Raw Node Graph
#SMX #21C1 @minderwinter
 A layout algorithm uses the weights of the
connections/edges to re-arrange the
nodes.
 Usually...
#SMX #21C1 @minderwinter
 Nodes that refer to
each other often are
now placed close
together in 2D space.
 Two central
c...
#SMX #21C1 @minderwinter
 To make this graph more useful, we’d like
to map a metric to node size
 The metric should give...
#SMX #21C1 @minderwinter
 Degree: the number of a node’s
connections.
 In-Degree: the number of a node’s
incoming connec...
#SMX #21C1 @minderwinter
 A
 Degree = 2
 In-Degree = 0
 Out-Degree = 2
Degree Example
A B C
A 0 1 1
B 0 0 2
C 0 1 0
#SMX #21C1 @minderwinter
 B
 Degree = 1
 In-Degree = 0
 Out-Degree = 1
Degree Example
A B C
A 0 1 1
B 0 0 2
C 0 1 0
#SMX #21C1 @minderwinter
 Weighted Degree: the number of a node’s
connections multiplied by their weights.
 In-Degree: t...
#SMX #21C1 @minderwinter
 B
 Weighted Degree = 2
 In-Degree = 0
 Out-Degree = 2
Weighted Degree Example
A B C
A 0 1 1
...
#SMX #21C1 @minderwinter
 The most important nodes are the ones generating incremental
conversions
 Conceptually, they g...
#SMX #21C1 @minderwinter
(Weighted Out-degree + Last Click) – Weighted In-Degree
 This metric gives us an indication of n...
#SMX #21C1 @minderwinter
 Nodes that generate
more incremental
conversions are
larger
 Caveat: flawed
tracking means thi...
#SMX #21C1 @minderwinter
 Positioning tells us which nodes are closely
connected, and size tells us how well
nodes genera...
#SMX #21C1 @minderwinter
 The lower a node is in the conversion
funnel, the more last clicks it should have
 The higher ...
#SMX #21C1 @minderwinter
Last Click / (Weighted Out-degree + Last Click)
 0 for nodes with no last click
 1 for nodes wi...
#SMX #21C1 @minderwinter
 Nodes high in the
funnel are redder
 Nodes lower in the
funnel are bluer
 In-between nodes
ar...
#SMX #21C1 @minderwinter
The Final Result
#SMX #21C1 @minderwinter
 Proximity tells you how often channels
interact
 Color tells you a channel/campaign’s
position...
#SMX #21C1 @minderwinter
 Identify “sinks”
 Sinks are blueish.
 These kinds of channels
are at the end of the
conversio...
#SMX #21C1 @minderwinter
 Identify “sources”:
 Reddish
 Tend to be earlier in
the conversion path
 Undervalued by last...
#SMX #21C1 @minderwinter
 Identify “assistors”:
 Pale, or sometimes
white
 Beware of small
assistors
 Tend to be midwa...
#SMX #21C1 @minderwinter
 Display
 Retargeting
 Direct Buy
 Behavioral
 Paid Search
 Branded
 Unbranded
 Organic S...
#SMX #21C1 @minderwinter
 Display
 Retargeting (Assistor)
 Direct Buy (Source)
 Behavioral
(Source/Assistor)
 Paid Se...
#SMX #21C1 @minderwinter
 Depending on your sales cycle, channels &
campaigns may function differently in the
conversion ...
#SMX #21C1 @minderwinter
 Nodes with little visibility are hard to
interpret:
 Organic: because of (not provided), its a...
#SMX #21C1 @minderwinter
 Select an attribution model that fits your
conversion process
 Sources are under valued by bot...
#SMX #21C1 @minderwinter
THANK YOU!
Charles Midwinter
Associate Director of Marketing Strategy
Collegis Education
traffict...
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Visualizing Attribution in Living Color

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From the SMX Advanced Conference in Seattle, Washington, June 2-3, 2015. SESSION: Visualizing Attribution In Living Color. PRESENTATION: Visualizing Attribution in Living Color - Given by Charles Midwinter, @Minderwinter - Globe Education Network, Director of Paid Digital Media & Analytics. #SMX #21C1

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Visualizing Attribution in Living Color

  1. 1. #SMX #21C1 @minderwinter Charles Midwinter, Collegis Education Visualizing Attribution in Living Color
  2. 2. #SMX #21C1 @minderwinter  When multiple channels or tactics assist with a conversion, an attribution model is the set of rules we use to “attribute” portions of the conversion to each assisting touch-point.  But you already knew that… What is Attribution (review, obviously)?
  3. 3. #SMX #21C1 @minderwinter  Last Interaction  Last Non-direct Click  Last AdWords Click  First Interaction  Linear  Time Decay  Position Based Google Analytics Attribution Models
  4. 4. #SMX #21C1 @minderwinter Almost anything is better than “Last Click,” but black boxes aren’t much better.  No visibility on the details of the attribution calculation  Possible pitfalls with certain channels  Too many groundless assumptions required The Problem with Out-of-the-Box Attribution Models
  5. 5. #SMX #21C1 @minderwinter If you want to understand multi-channel attribution, the “multi-channel attribution funnel” reports in Google Analytics are your first stop.  Take a look at the “top conversion paths” report  This is great information, but how to summarize it at a high level? Google Analytics & Channel/Tactic Interactions
  6. 6. #SMX #21C1 @minderwinter The object that can summarize these conversion paths is called an “edge matrix.”  Usually used for the analysis of networks (eg. social networks)  Encodes the connections among entities  Can be visualized as a “node graph” with open source software (Gephi) Edge Matrices
  7. 7. #SMX #21C1 @minderwinter Consider the following conversion paths:  A > C > B > C  A > B  B > C Edge Matrix Example 1/3
  8. 8. #SMX #21C1 @minderwinter In words  A  referred to C once  referred to B once  B  referred to C twice  C  referred to B once Edge Matrix Example 2/3
  9. 9. #SMX #21C1 @minderwinter As an “Edge Matrix” Edge Matrix Example 3/3 A B C A 0 1 1 B 0 0 2 C 0 1 0
  10. 10. #SMX #21C1 @minderwinter Just use my handy dandy Python script.  Go to:  traffictheory.org/smx-2015  Download the script  Make sure you have Python 2.7 installed (not Python 3!)  Follow the instructions at the URL above to run. MCF Top Conversion Paths to Edge Matrix
  11. 11. #SMX #21C1 @minderwinter To visualize the “Edge Matrix” as a Node Graph, you’ll need Gephi, open source graph software.  Open the “edge_matrix.csv” file created by the Python script (see website for more details)  Import the “last_click.csv” file created by the Python script (see website for more details) Turning an Edge Matrix into a Node Graph
  12. 12. #SMX #21C1 @minderwinter  How do we turn this spaghetti into something useful? The Raw Node Graph
  13. 13. #SMX #21C1 @minderwinter  A layout algorithm uses the weights of the connections/edges to re-arrange the nodes.  Usually physics-based, involving a gravitation-like attraction that scales with the edge weights between nodes, and often a repulsion that separates weakly connected nodes. Layout Algorithms
  14. 14. #SMX #21C1 @minderwinter  Nodes that refer to each other often are now placed close together in 2D space.  Two central communities of nodes are identifiable (“direct/(none)” and “google/organic”) The Result of Layout Algorithm “Force Atlas 2”
  15. 15. #SMX #21C1 @minderwinter  To make this graph more useful, we’d like to map a metric to node size  The metric should give us some indication of the node’s importance to the conversion process  In order to proceed, we should understand a bit more about the node graph Measuring Node Importance
  16. 16. #SMX #21C1 @minderwinter  Degree: the number of a node’s connections.  In-Degree: the number of a node’s incoming connections  Out-Degree: the number of a node’s out- going connections Degree
  17. 17. #SMX #21C1 @minderwinter  A  Degree = 2  In-Degree = 0  Out-Degree = 2 Degree Example A B C A 0 1 1 B 0 0 2 C 0 1 0
  18. 18. #SMX #21C1 @minderwinter  B  Degree = 1  In-Degree = 0  Out-Degree = 1 Degree Example A B C A 0 1 1 B 0 0 2 C 0 1 0
  19. 19. #SMX #21C1 @minderwinter  Weighted Degree: the number of a node’s connections multiplied by their weights.  In-Degree: the number of a node’s incoming connections multiplied by their weights.  Out-Degree: the number of a node’s out- going connections multiplied by their weights. Weighted Degree
  20. 20. #SMX #21C1 @minderwinter  B  Weighted Degree = 2  In-Degree = 0  Out-Degree = 2 Weighted Degree Example A B C A 0 1 1 B 0 0 2 C 0 1 0
  21. 21. #SMX #21C1 @minderwinter  The most important nodes are the ones generating incremental conversions  Conceptually, they generate a net output.  A node that gets no in-bound connections, but has many out- bound connections is a source of conversions, and should be highly valued.  A node that generates a lot of last-click conversions has value, but its net output should be adjusted so that in-bound connections are subtracted.  A node that has as many in-bound connections as it does last- click/out-bound connections is adding little value from an incremental perspective. Assessing Node (Campaign or Source/Medium) Importance
  22. 22. #SMX #21C1 @minderwinter (Weighted Out-degree + Last Click) – Weighted In-Degree  This metric gives us an indication of node importance from an incremental conversion perspective. Net Output
  23. 23. #SMX #21C1 @minderwinter  Nodes that generate more incremental conversions are larger  Caveat: flawed tracking means this metric is far from perfect Mapping “Net Output” to Node Size
  24. 24. #SMX #21C1 @minderwinter  Positioning tells us which nodes are closely connected, and size tells us how well nodes generate incremental conversions  It would also be nice to know how each node tends to assist in the conversion process: does it produce last clicks, or is it higher in the funnel? Assessing Node Function
  25. 25. #SMX #21C1 @minderwinter  The lower a node is in the conversion funnel, the more last clicks it should have  The higher a node is in the funnel, the more likely it is to push traffic to other nodes (high weighted out-degree) Funnel Position 1/2
  26. 26. #SMX #21C1 @minderwinter Last Click / (Weighted Out-degree + Last Click)  0 for nodes with no last click  1 for nodes with all last click  Varies from 0 to 1 as ratio of last click to weighted out-degree increases Funnel Position 2/2
  27. 27. #SMX #21C1 @minderwinter  Nodes high in the funnel are redder  Nodes lower in the funnel are bluer  In-between nodes are lighter in color, sometimes almost white. Mapping Funnel Position to Node Color
  28. 28. #SMX #21C1 @minderwinter The Final Result
  29. 29. #SMX #21C1 @minderwinter  Proximity tells you how often channels interact  Color tells you a channel/campaign’s position in the funnel  Size tells you how many incremental conversions are likely generated by a channel/campaign How to Interpret the Result
  30. 30. #SMX #21C1 @minderwinter  Identify “sinks”  Sinks are blueish.  These kinds of channels are at the end of the conversion path  They are lynch pins in the network, fed by channels higher in the funnel  Overvalued by last click Sinks
  31. 31. #SMX #21C1 @minderwinter  Identify “sources”:  Reddish  Tend to be earlier in the conversion path  Undervalued by last click Sources
  32. 32. #SMX #21C1 @minderwinter  Identify “assistors”:  Pale, or sometimes white  Beware of small assistors  Tend to be midway in the conversion path  Undervalued by last click, but can be overvalued by other models Assistors
  33. 33. #SMX #21C1 @minderwinter  Display  Retargeting  Direct Buy  Behavioral  Paid Search  Branded  Unbranded  Organic Search  Referral  Social  Direct Source, Sink, or Assistor?
  34. 34. #SMX #21C1 @minderwinter  Display  Retargeting (Assistor)  Direct Buy (Source)  Behavioral (Source/Assistor)  Paid Search  Branded (Sink)  Unbranded (Source/Assistor)  Organic Search (Assistor/Sink)  Referral (Source/Assistor)  Social (Assistor)  Direct (Assistor/Sink) Source, Sink, or Assistor?
  35. 35. #SMX #21C1 @minderwinter  Depending on your sales cycle, channels & campaigns may function differently in the conversion funnel Results May Vary
  36. 36. #SMX #21C1 @minderwinter  Nodes with little visibility are hard to interpret:  Organic: because of (not provided), its a mix of branded and unbranded. Its “Funnel Position” will be determined by the strength of your brand and the amount of unbranded organic traffic you receive.  Direct: can skew your results. We know it contains all kinds of poorly tracked traffic. Sometimes, I just go ahead and remove direct from the graph. Caveats
  37. 37. #SMX #21C1 @minderwinter  Select an attribution model that fits your conversion process  Sources are under valued by both last click and time decay, for example.  Identify outliers and understand what they say about your mix (discover fraud)  Use the visualization rhetorically to justify budget for exposure tactics How to Make This Actionable
  38. 38. #SMX #21C1 @minderwinter THANK YOU! Charles Midwinter Associate Director of Marketing Strategy Collegis Education traffictheory.org/smx-2015

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